<<<<<<< HEAD Pandas Profiling Report

Overview

Dataset statistics

Number of variables10
Number of observations46180
Missing cells1804
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory80.0 B

Variable types

Numeric7
Categorical3

Alerts

Fecha has a high cardinality: 1846 distinct values High cardinality
Fecha_Entrega has a high cardinality: 2146 distinct values High cardinality
IdVenta is highly correlated with IdSucursal and 1 other fieldsHigh correlation
IdSucursal is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdEmpleado is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdVenta is highly correlated with IdSucursal and 1 other fieldsHigh correlation
IdSucursal is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdEmpleado is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdVenta is highly correlated with IdSucursal and 1 other fieldsHigh correlation
IdSucursal is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdEmpleado is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdVenta is highly correlated with IdSucursal and 1 other fieldsHigh correlation
IdSucursal is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdEmpleado is highly correlated with IdVenta and 1 other fieldsHigh correlation
Precio has 920 (2.0%) missing values Missing
Cantidad has 884 (1.9%) missing values Missing
Precio is highly skewed (γ1 = 109.2895317) Skewed
Cantidad is highly skewed (γ1 = 28.27378552) Skewed
IdVenta has unique values Unique

Reproduction

Analysis started2022-06-23 01:54:06.893874
Analysis finished2022-06-23 01:54:15.790947
Duration8.9 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

IdVenta
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct46180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23151.53428
Minimum1
Maximum47600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T20:54:15.905948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Overview

Dataset statistics

Number of variables10
Number of observations46180
Missing cells1804
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory80.0 B

Variable types

Numeric7
Categorical3

Alerts

Fecha has a high cardinality: 1846 distinct values High cardinality
Fecha_Entrega has a high cardinality: 2146 distinct values High cardinality
IdVenta is highly correlated with IdSucursal and 1 other fieldsHigh correlation
IdSucursal is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdEmpleado is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdVenta is highly correlated with IdSucursal and 1 other fieldsHigh correlation
IdSucursal is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdEmpleado is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdVenta is highly correlated with IdSucursal and 1 other fieldsHigh correlation
IdSucursal is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdEmpleado is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdVenta is highly correlated with IdSucursal and 1 other fieldsHigh correlation
IdSucursal is highly correlated with IdVenta and 1 other fieldsHigh correlation
IdEmpleado is highly correlated with IdVenta and 1 other fieldsHigh correlation
Precio has 920 (2.0%) missing values Missing
Cantidad has 884 (1.9%) missing values Missing
Precio is highly skewed (γ1 = 109.2895317) Skewed
Cantidad is highly skewed (γ1 = 28.27378552) Skewed
IdVenta has unique values Unique

Reproduction

Analysis started2022-06-23 04:53:18.024244
Analysis finished2022-06-23 04:53:32.654537
Duration14.63 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

IdVenta
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct46180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23151.53428
Minimum1
Maximum47600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T23:53:32.772533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2309.95
Q111545.75
median23090.5
Q334635.25
95-th percentile44444.05
Maximum47600
Range47599
Interquartile range (IQR)23089.5

Descriptive statistics

Standard deviation13432.58594
Coefficient of variation (CV)0.58020284
Kurtosis-1.170883379
Mean23151.53428
Median Absolute Deviation (MAD)11545
Skewness0.02392432839
Sum1069137853
Variance180434365
MonotonicityStrictly increasing
2022-06-22T20:54:16.043949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2309.95
Q111545.75
median23090.5
Q334635.25
95-th percentile44444.05
Maximum47600
Range47599
Interquartile range (IQR)23089.5

Descriptive statistics

Standard deviation13432.58594
Coefficient of variation (CV)0.58020284
Kurtosis-1.170883379
Mean23151.53428
Median Absolute Deviation (MAD)11545
Skewness0.02392432839
Sum1069137853
Variance180434365
MonotonicityStrictly increasing
2022-06-22T23:53:32.995372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
307811
 
< 0.1%
307831
 
< 0.1%
307841
 
< 0.1%
307851
 
< 0.1%
307861
 
< 0.1%
307871
 
< 0.1%
307881
 
< 0.1%
307891
 
< 0.1%
307901
 
< 0.1%
Other values (46170)46170
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
476001
< 0.1%
475991
< 0.1%
475981
< 0.1%
475971
< 0.1%
475961
< 0.1%
475951
< 0.1%
475941
< 0.1%
475931
< 0.1%
475921
< 0.1%
475911
< 0.1%

Fecha
Categorical

HIGH CARDINALITY

Distinct1846
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size360.9 KiB
2020-11-19
 
88
2020-03-31
 
79
2020-01-01
 
72
2016-09-27
 
71
2020-09-09
 
70
Other values (1841)
45800 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters461800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row2018-03-09
2nd row2018-12-28
3rd row2016-03-28
4th row2017-10-23
5th row2017-11-22

Common Values

ValueCountFrequency (%)
2020-11-1988
 
0.2%
2020-03-3179
 
0.2%
2020-01-0172
 
0.2%
2016-09-2771
 
0.2%
2020-09-0970
 
0.2%
2020-05-2769
 
0.1%
2020-09-1169
 
0.1%
2020-04-1369
 
0.1%
2020-03-3067
 
0.1%
2020-01-0966
 
0.1%
Other values (1836)45460
98.4%

Length

2022-06-22T20:54:16.154950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
307811
 
< 0.1%
307831
 
< 0.1%
307841
 
< 0.1%
307851
 
< 0.1%
307861
 
< 0.1%
307871
 
< 0.1%
307881
 
< 0.1%
307891
 
< 0.1%
307901
 
< 0.1%
Other values (46170)46170
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
476001
< 0.1%
475991
< 0.1%
475981
< 0.1%
475971
< 0.1%
475961
< 0.1%
475951
< 0.1%
475941
< 0.1%
475931
< 0.1%
475921
< 0.1%
475911
< 0.1%

Fecha
Categorical

HIGH CARDINALITY

Distinct1846
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size360.9 KiB
2020-11-19
 
88
2020-03-31
 
79
2020-01-01
 
72
2016-09-27
 
71
2020-09-09
 
70
Other values (1841)
45800 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters461800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row2018-03-09
2nd row2018-12-28
3rd row2016-03-28
4th row2017-10-23
5th row2017-11-22

Common Values

ValueCountFrequency (%)
2020-11-1988
 
0.2%
2020-03-3179
 
0.2%
2020-01-0172
 
0.2%
2016-09-2771
 
0.2%
2020-09-0970
 
0.2%
2020-05-2769
 
0.1%
2020-09-1169
 
0.1%
2020-04-1369
 
0.1%
2020-03-3067
 
0.1%
2020-01-0966
 
0.1%
Other values (1836)45460
98.4%

Length

2022-06-22T23:53:33.183374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-11-1988
 
0.2%
2020-03-3179
 
0.2%
2020-01-0172
 
0.2%
2016-09-2771
 
0.2%
2020-09-0970
 
0.2%
2020-05-2769
 
0.1%
2020-09-1169
 
0.1%
2020-04-1369
 
0.1%
2020-03-3067
 
0.1%
2020-01-0966
 
0.1%
Other values (1836)45460
98.4%

Most occurring characters

ValueCountFrequency (%)
0112621
24.4%
-92360
20.0%
281554
17.7%
176562
16.6%
716154
 
3.5%
915904
 
3.4%
515797
 
3.4%
815706
 
3.4%
615372
 
3.3%
311249
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number369440
80.0%
Dash Punctuation92360
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112621
30.5%
281554
22.1%
176562
20.7%
716154
 
4.4%
915904
 
4.3%
515797
 
4.3%
815706
 
4.3%
615372
 
4.2%
311249
 
3.0%
48521
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
-92360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common461800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0112621
24.4%
-92360
20.0%
281554
17.7%
176562
16.6%
716154
 
3.5%
915904
 
3.4%
515797
 
3.4%
815706
 
3.4%
615372
 
3.3%
311249
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII461800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112621
24.4%
-92360
20.0%
281554
17.7%
176562
16.6%
716154
 
3.5%
915904
 
3.4%
515797
 
3.4%
815706
 
3.4%
615372
 
3.3%
311249
 
2.4%

Fecha_Entrega
Categorical

HIGH CARDINALITY

Distinct2146
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size360.9 KiB
2018-06-19
 
71
2020-01-05
 
69
2020-01-24
 
66
2019-09-14
 
66
2020-02-25
 
61
Other values (2141)
45847 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters461800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st row2018-03-17
2nd row2018-12-29
3rd row2016-03-31
4th row2017-10-24
5th row2017-11-25

Common Values

ValueCountFrequency (%)
2018-06-1971
 
0.2%
2020-01-0569
 
0.1%
2020-01-2466
 
0.1%
2019-09-1466
 
0.1%
2020-02-2561
 
0.1%
2020-05-1060
 
0.1%
2020-04-2559
 
0.1%
2019-02-1659
 
0.1%
2019-08-0858
 
0.1%
2020-02-1655
 
0.1%
Other values (2136)45556
98.6%

Length

2022-06-22T20:54:16.247950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-11-1988
 
0.2%
2020-03-3179
 
0.2%
2020-01-0172
 
0.2%
2016-09-2771
 
0.2%
2020-09-0970
 
0.2%
2020-05-2769
 
0.1%
2020-09-1169
 
0.1%
2020-04-1369
 
0.1%
2020-03-3067
 
0.1%
2020-01-0966
 
0.1%
Other values (1836)45460
98.4%

Most occurring characters

ValueCountFrequency (%)
0112621
24.4%
-92360
20.0%
281554
17.7%
176562
16.6%
716154
 
3.5%
915904
 
3.4%
515797
 
3.4%
815706
 
3.4%
615372
 
3.3%
311249
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number369440
80.0%
Dash Punctuation92360
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112621
30.5%
281554
22.1%
176562
20.7%
716154
 
4.4%
915904
 
4.3%
515797
 
4.3%
815706
 
4.3%
615372
 
4.2%
311249
 
3.0%
48521
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
-92360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common461800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0112621
24.4%
-92360
20.0%
281554
17.7%
176562
16.6%
716154
 
3.5%
915904
 
3.4%
515797
 
3.4%
815706
 
3.4%
615372
 
3.3%
311249
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII461800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112621
24.4%
-92360
20.0%
281554
17.7%
176562
16.6%
716154
 
3.5%
915904
 
3.4%
515797
 
3.4%
815706
 
3.4%
615372
 
3.3%
311249
 
2.4%

Fecha_Entrega
Categorical

HIGH CARDINALITY

Distinct2146
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size360.9 KiB
2018-06-19
 
71
2020-01-05
 
69
2020-01-24
 
66
2019-09-14
 
66
2020-02-25
 
61
Other values (2141)
45847 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters461800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st row2018-03-17
2nd row2018-12-29
3rd row2016-03-31
4th row2017-10-24
5th row2017-11-25

Common Values

ValueCountFrequency (%)
2018-06-1971
 
0.2%
2020-01-0569
 
0.1%
2020-01-2466
 
0.1%
2019-09-1466
 
0.1%
2020-02-2561
 
0.1%
2020-05-1060
 
0.1%
2020-04-2559
 
0.1%
2019-02-1659
 
0.1%
2019-08-0858
 
0.1%
2020-02-1655
 
0.1%
Other values (2136)45556
98.6%

Length

2022-06-22T23:53:33.343512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-06-1971
 
0.2%
2020-01-0569
 
0.1%
2020-01-2466
 
0.1%
2019-09-1466
 
0.1%
2020-02-2561
 
0.1%
2020-05-1060
 
0.1%
2020-04-2559
 
0.1%
2019-02-1659
 
0.1%
2019-08-0858
 
0.1%
2020-04-0555
 
0.1%
Other values (2136)45556
98.6%

Most occurring characters

ValueCountFrequency (%)
0112459
24.4%
-92360
20.0%
281902
17.7%
176500
16.6%
516082
 
3.5%
715998
 
3.5%
915809
 
3.4%
615771
 
3.4%
815314
 
3.3%
310836
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number369440
80.0%
Dash Punctuation92360
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112459
30.4%
281902
22.2%
176500
20.7%
516082
 
4.4%
715998
 
4.3%
915809
 
4.3%
615771
 
4.3%
815314
 
4.1%
310836
 
2.9%
48769
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
-92360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common461800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0112459
24.4%
-92360
20.0%
281902
17.7%
176500
16.6%
516082
 
3.5%
715998
 
3.5%
915809
 
3.4%
615771
 
3.4%
815314
 
3.3%
310836
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII461800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112459
24.4%
-92360
20.0%
281902
17.7%
176500
16.6%
516082
 
3.5%
715998
 
3.5%
915809
 
3.4%
615771
 
3.4%
815314
 
3.3%
310836
 
2.3%

IdCanal
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size360.9 KiB
2
20517 
3
13248 
1
12415 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46180
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

Length

2022-06-22T20:54:16.340746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-06-1971
 
0.2%
2020-01-0569
 
0.1%
2020-01-2466
 
0.1%
2019-09-1466
 
0.1%
2020-02-2561
 
0.1%
2020-05-1060
 
0.1%
2020-04-2559
 
0.1%
2019-02-1659
 
0.1%
2019-08-0858
 
0.1%
2020-04-0555
 
0.1%
Other values (2136)45556
98.6%

Most occurring characters

ValueCountFrequency (%)
0112459
24.4%
-92360
20.0%
281902
17.7%
176500
16.6%
516082
 
3.5%
715998
 
3.5%
915809
 
3.4%
615771
 
3.4%
815314
 
3.3%
310836
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number369440
80.0%
Dash Punctuation92360
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112459
30.4%
281902
22.2%
176500
20.7%
516082
 
4.4%
715998
 
4.3%
915809
 
4.3%
615771
 
4.3%
815314
 
4.1%
310836
 
2.9%
48769
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
-92360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common461800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0112459
24.4%
-92360
20.0%
281902
17.7%
176500
16.6%
516082
 
3.5%
715998
 
3.5%
915809
 
3.4%
615771
 
3.4%
815314
 
3.3%
310836
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII461800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112459
24.4%
-92360
20.0%
281902
17.7%
176500
16.6%
516082
 
3.5%
715998
 
3.5%
915809
 
3.4%
615771
 
3.4%
815314
 
3.3%
310836
 
2.3%

IdCanal
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size360.9 KiB
2
20517 
3
13248 
1
12415 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46180
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

Length

2022-06-22T23:53:33.496509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T20:54:16.437750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-22T23:53:33.660532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

Most occurring characters

ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

Most occurring scripts

ValueCountFrequency (%)
Common46180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII46180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

IdCliente
Real number (ℝ≥0)

Distinct2961
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.635232
Minimum1
Maximum3407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T20:54:16.543773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

Most occurring characters

ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

Most occurring scripts

ValueCountFrequency (%)
Common46180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII46180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220517
44.4%
313248
28.7%
112415
26.9%

IdCliente
Real number (ℝ≥0)

Distinct2961
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.635232
Minimum1
Maximum3407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T23:53:33.853531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile141
Q1691
median1359
Q32273
95-th percentile3203
Maximum3407
Range3406
Interquartile range (IQR)1582

Descriptive statistics

Standard deviation972.5364043
Coefficient of variation (CV)0.6472205521
Kurtosis-1.020096277
Mean1502.635232
Median Absolute Deviation (MAD)730
Skewness0.3681335027
Sum69391695
Variance945827.0576
MonotonicityNot monotonic
2022-06-22T20:54:16.669747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile141
Q1691
median1359
Q32273
95-th percentile3203
Maximum3407
Range3406
Interquartile range (IQR)1582

Descriptive statistics

Standard deviation972.5364043
Coefficient of variation (CV)0.6472205521
Kurtosis-1.020096277
Mean1502.635232
Median Absolute Deviation (MAD)730
Skewness0.3681335027
Sum69391695
Variance945827.0576
MonotonicityNot monotonic
2022-06-22T23:53:34.073512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63455
 
0.1%
72152
 
0.1%
129249
 
0.1%
16448
 
0.1%
90048
 
0.1%
38546
 
0.1%
114145
 
0.1%
327945
 
0.1%
48344
 
0.1%
6044
 
0.1%
Other values (2951)45704
99.0%
ValueCountFrequency (%)
127
0.1%
229
0.1%
323
< 0.1%
429
0.1%
517
< 0.1%
615
< 0.1%
719
< 0.1%
821
< 0.1%
930
0.1%
104
 
< 0.1%
ValueCountFrequency (%)
340725
0.1%
340632
0.1%
340520
< 0.1%
340426
0.1%
340319
< 0.1%
340214
< 0.1%
340114
< 0.1%
34009
 
< 0.1%
339926
0.1%
339810
 
< 0.1%

IdSucursal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.78235167
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T20:54:16.794747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63455
 
0.1%
72152
 
0.1%
129249
 
0.1%
16448
 
0.1%
90048
 
0.1%
38546
 
0.1%
114145
 
0.1%
327945
 
0.1%
48344
 
0.1%
6044
 
0.1%
Other values (2951)45704
99.0%
ValueCountFrequency (%)
127
0.1%
229
0.1%
323
< 0.1%
429
0.1%
517
< 0.1%
615
< 0.1%
719
< 0.1%
821
< 0.1%
930
0.1%
104
 
< 0.1%
ValueCountFrequency (%)
340725
0.1%
340632
0.1%
340520
< 0.1%
340426
0.1%
340319
< 0.1%
340214
< 0.1%
340114
< 0.1%
34009
 
< 0.1%
339926
0.1%
339810
 
< 0.1%

IdSucursal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.78235167
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T23:53:34.294531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.876539033
Coefficient of variation (CV)0.6004821988
Kurtosis-1.240470593
Mean14.78235167
Median Absolute Deviation (MAD)8
Skewness0.1027025144
Sum682649
Variance78.7929452
MonotonicityNot monotonic
2022-06-22T20:54:16.907747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.876539033
Coefficient of variation (CV)0.6004821988
Kurtosis-1.240470593
Mean14.78235167
Median Absolute Deviation (MAD)8
Skewness0.1027025144
Sum682649
Variance78.7929452
MonotonicityNot monotonic
2022-06-22T23:53:34.479511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
73262
 
7.1%
12340
 
5.1%
242204
 
4.8%
102180
 
4.7%
252154
 
4.7%
262124
 
4.6%
42096
 
4.5%
232051
 
4.4%
152013
 
4.4%
21961
 
4.2%
Other values (20)23795
51.5%
ValueCountFrequency (%)
12340
5.1%
21961
4.2%
31054
 
2.3%
42096
4.5%
5939
 
2.0%
61497
3.2%
73262
7.1%
81759
3.8%
9877
 
1.9%
102180
4.7%
ValueCountFrequency (%)
31800
 
1.7%
301277
2.8%
29973
2.1%
271448
3.1%
262124
4.6%
252154
4.7%
242204
4.8%
232051
4.4%
22605
 
1.3%
211625
3.5%

IdEmpleado
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct249
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2488.830858
Minimum1011
Maximum3979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T20:54:17.045747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
73262
 
7.1%
12340
 
5.1%
242204
 
4.8%
102180
 
4.7%
252154
 
4.7%
262124
 
4.6%
42096
 
4.5%
232051
 
4.4%
152013
 
4.4%
21961
 
4.2%
Other values (20)23795
51.5%
ValueCountFrequency (%)
12340
5.1%
21961
4.2%
31054
 
2.3%
42096
4.5%
5939
 
2.0%
61497
3.2%
73262
7.1%
81759
3.8%
9877
 
1.9%
102180
4.7%
ValueCountFrequency (%)
31800
 
1.7%
301277
2.8%
29973
2.1%
271448
3.1%
262124
4.6%
252154
4.7%
242204
4.8%
232051
4.4%
22605
 
1.3%
211625
3.5%

IdEmpleado
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct249
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2488.830858
Minimum1011
Maximum3979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T23:53:34.693512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1011
5-th percentile1079
Q11574
median2468
Q33433
95-th percentile3875
Maximum3979
Range2968
Interquartile range (IQR)1859

Descriptive statistics

Standard deviation960.2319333
Coefficient of variation (CV)0.3858164689
Kurtosis-1.537934081
Mean2488.830858
Median Absolute Deviation (MAD)912
Skewness0.04120308887
Sum114934209
Variance922045.3656
MonotonicityNot monotonic
2022-06-22T20:54:17.168746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1011
5-th percentile1079
Q11574
median2468
Q33433
95-th percentile3875
Maximum3979
Range2968
Interquartile range (IQR)1859

Descriptive statistics

Standard deviation960.2319333
Coefficient of variation (CV)0.3858164689
Kurtosis-1.537934081
Mean2488.830858
Median Absolute Deviation (MAD)912
Skewness0.04120308887
Sum114934209
Variance922045.3656
MonotonicityNot monotonic
2022-06-22T23:53:34.928511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1675620
 
1.3%
3504531
 
1.1%
3603531
 
1.1%
1674525
 
1.1%
3186519
 
1.1%
3346503
 
1.1%
3875485
 
1.1%
1961328
 
0.7%
1676323
 
0.7%
1967323
 
0.7%
Other values (239)41492
89.8%
ValueCountFrequency (%)
1011246
0.5%
1012299
0.6%
1041287
0.6%
1054293
0.6%
1055300
0.6%
105614
 
< 0.1%
106713
 
< 0.1%
1068282
0.6%
1075288
0.6%
107614
 
< 0.1%
ValueCountFrequency (%)
397917
 
< 0.1%
39758
 
< 0.1%
3950271
0.6%
3929279
0.6%
3919281
0.6%
3917252
0.5%
3916256
0.6%
39058
 
< 0.1%
3886233
0.5%
387713
 
< 0.1%

IdProducto
Real number (ℝ≥0)

Distinct283
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42901.26438
Minimum42737
Maximum43043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T20:54:17.301773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1675620
 
1.3%
3504531
 
1.1%
3603531
 
1.1%
1674525
 
1.1%
3186519
 
1.1%
3346503
 
1.1%
3875485
 
1.1%
1961328
 
0.7%
1676323
 
0.7%
1967323
 
0.7%
Other values (239)41492
89.8%
ValueCountFrequency (%)
1011246
0.5%
1012299
0.6%
1041287
0.6%
1054293
0.6%
1055300
0.6%
105614
 
< 0.1%
106713
 
< 0.1%
1068282
0.6%
1075288
0.6%
107614
 
< 0.1%
ValueCountFrequency (%)
397917
 
< 0.1%
39758
 
< 0.1%
3950271
0.6%
3929279
0.6%
3919281
0.6%
3917252
0.5%
3916256
0.6%
39058
 
< 0.1%
3886233
0.5%
387713
 
< 0.1%

IdProducto
Real number (ℝ≥0)

Distinct283
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42901.26438
Minimum42737
Maximum43043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T23:53:35.223513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum42737
5-th percentile42767
Q142833
median42902
Q342974
95-th percentile43030
Maximum43043
Range306
Interquartile range (IQR)141

Descriptive statistics

Standard deviation83.58848656
Coefficient of variation (CV)0.001948392146
Kurtosis-1.155513285
Mean42901.26438
Median Absolute Deviation (MAD)71
Skewness-0.05958632538
Sum1981180389
Variance6987.035085
MonotonicityNot monotonic
2022-06-22T20:54:17.427747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum42737
5-th percentile42767
Q142833
median42902
Q342974
95-th percentile43030
Maximum43043
Range306
Interquartile range (IQR)141

Descriptive statistics

Standard deviation83.58848656
Coefficient of variation (CV)0.001948392146
Kurtosis-1.155513285
Mean42901.26438
Median Absolute Deviation (MAD)71
Skewness-0.05958632538
Sum1981180389
Variance6987.035085
MonotonicityNot monotonic
2022-06-22T23:53:35.517513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42975235
 
0.5%
42838230
 
0.5%
42915225
 
0.5%
43031221
 
0.5%
42931220
 
0.5%
42886209
 
0.5%
43029208
 
0.5%
42840207
 
0.4%
42812206
 
0.4%
42779205
 
0.4%
Other values (273)44014
95.3%
ValueCountFrequency (%)
42737135
0.3%
42754175
0.4%
42755153
0.3%
42756150
0.3%
42757195
0.4%
42758160
0.3%
42759151
0.3%
42760131
0.3%
42761125
0.3%
42762158
0.3%
ValueCountFrequency (%)
43043186
0.4%
43042156
0.3%
43041138
0.3%
43040138
0.3%
43039156
0.3%
43038159
0.3%
43037181
0.4%
43036187
0.4%
43035188
0.4%
43034171
0.4%

Precio
Real number (ℝ≥0)

MISSING
SKEWED

Distinct451
Distinct (%)1.0%
Missing920
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean5876.587015
Minimum3
Maximum33739200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T20:54:17.565389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42975235
 
0.5%
42838230
 
0.5%
42915225
 
0.5%
43031221
 
0.5%
42931220
 
0.5%
42886209
 
0.5%
43029208
 
0.5%
42840207
 
0.4%
42812206
 
0.4%
42779205
 
0.4%
Other values (273)44014
95.3%
ValueCountFrequency (%)
42737135
0.3%
42754175
0.4%
42755153
0.3%
42756150
0.3%
42757195
0.4%
42758160
0.3%
42759151
0.3%
42760131
0.3%
42761125
0.3%
42762158
0.3%
ValueCountFrequency (%)
43043186
0.4%
43042156
0.3%
43041138
0.3%
43040138
0.3%
43039156
0.3%
43038159
0.3%
43037181
0.4%
43036187
0.4%
43035188
0.4%
43034171
0.4%

Precio
Real number (ℝ≥0)

MISSING
SKEWED

Distinct451
Distinct (%)1.0%
Missing920
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean5876.587015
Minimum3
Maximum33739200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T23:53:35.772513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile156
Q1438
median654
Q31789
95-th percentile3978
Maximum33739200
Range33739197
Interquartile range (IQR)1351

Descriptive statistics

Standard deviation251065.064
Coefficient of variation (CV)42.72293821
Kurtosis12531.59967
Mean5876.587015
Median Absolute Deviation (MAD)396
Skewness109.2895317
Sum265974328.3
Variance6.303366634 × 1010
MonotonicityNot monotonic
2022-06-22T20:54:17.691395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile156
Q1438
median654
Q31789
95-th percentile3978
Maximum33739200
Range33739197
Interquartile range (IQR)1351

Descriptive statistics

Standard deviation251065.064
Coefficient of variation (CV)42.72293821
Kurtosis12531.59967
Mean5876.587015
Median Absolute Deviation (MAD)396
Skewness109.2895317
Sum265974328.3
Variance6.303366634 × 1010
MonotonicityNot monotonic
2022-06-22T23:53:36.019521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5601530
 
3.3%
5151529
 
3.3%
4981336
 
2.9%
1991090
 
2.4%
353.32610
 
1.3%
456508
 
1.1%
818.84474
 
1.0%
354437
 
0.9%
1159365
 
0.8%
387360
 
0.8%
Other values (441)37021
80.2%
(Missing)920
 
2.0%
ValueCountFrequency (%)
3147
0.3%
13180
0.4%
52158
0.3%
79163
0.4%
91143
0.3%
111167
0.4%
112147
0.3%
113175
0.4%
114160
0.3%
123303
0.7%
ValueCountFrequency (%)
337392001
 
< 0.1%
255992001
 
< 0.1%
245800001
 
< 0.1%
201500001
 
< 0.1%
45600001
 
< 0.1%
15600001
 
< 0.1%
9555044
< 0.1%
8703643
< 0.1%
7011624
< 0.1%
6569863
< 0.1%

Cantidad
Real number (ℝ≥0)

MISSING
SKEWED

Distinct9
Distinct (%)< 0.1%
Missing884
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean2.388246203
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T20:54:17.803422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5601530
 
3.3%
5151529
 
3.3%
4981336
 
2.9%
1991090
 
2.4%
353.32610
 
1.3%
456508
 
1.1%
818.84474
 
1.0%
354437
 
0.9%
1159365
 
0.8%
387360
 
0.8%
Other values (441)37021
80.2%
(Missing)920
 
2.0%
ValueCountFrequency (%)
3147
0.3%
13180
0.4%
52158
0.3%
79163
0.4%
91143
0.3%
111167
0.4%
112147
0.3%
113175
0.4%
114160
0.3%
123303
0.7%
ValueCountFrequency (%)
337392001
 
< 0.1%
255992001
 
< 0.1%
245800001
 
< 0.1%
201500001
 
< 0.1%
45600001
 
< 0.1%
15600001
 
< 0.1%
9555044
< 0.1%
8703643
< 0.1%
7011624
< 0.1%
6569863
< 0.1%

Cantidad
Real number (ℝ≥0)

MISSING
SKEWED

Distinct9
Distinct (%)< 0.1%
Missing884
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean2.388246203
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size360.9 KiB
2022-06-22T23:53:36.223511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum300
Range299
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.562044651
Coefficient of variation (CV)1.491489716
Kurtosis1737.477923
Mean2.388246203
Median Absolute Deviation (MAD)1
Skewness28.27378552
Sum108178
Variance12.6881621
MonotonicityNot monotonic
2022-06-22T20:54:17.884394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum300
Range299
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.562044651
Coefficient of variation (CV)1.491489716
Kurtosis1737.477923
Mean2.388246203
Median Absolute Deviation (MAD)1
Skewness28.27378552
Sum108178
Variance12.6881621
MonotonicityNot monotonic
2022-06-22T23:53:36.384511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
222448
48.6%
311042
23.9%
110896
23.6%
20459
 
1.0%
30231
 
0.5%
10215
 
0.5%
2003
 
< 0.1%
1001
 
< 0.1%
3001
 
< 0.1%
(Missing)884
 
1.9%
ValueCountFrequency (%)
110896
23.6%
222448
48.6%
311042
23.9%
10215
 
0.5%
20459
 
1.0%
30231
 
0.5%
1001
 
< 0.1%
2003
 
< 0.1%
3001
 
< 0.1%
ValueCountFrequency (%)
3001
 
< 0.1%
2003
 
< 0.1%
1001
 
< 0.1%
30231
 
0.5%
20459
 
1.0%
10215
 
0.5%
311042
23.9%
222448
48.6%
110896
23.6%

Interactions

2022-06-22T20:54:14.246220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
222448
48.6%
311042
23.9%
110896
23.6%
20459
 
1.0%
30231
 
0.5%
10215
 
0.5%
2003
 
< 0.1%
1001
 
< 0.1%
3001
 
< 0.1%
(Missing)884
 
1.9%
ValueCountFrequency (%)
110896
23.6%
222448
48.6%
311042
23.9%
10215
 
0.5%
20459
 
1.0%
30231
 
0.5%
1001
 
< 0.1%
2003
 
< 0.1%
3001
 
< 0.1%
ValueCountFrequency (%)
3001
 
< 0.1%
2003
 
< 0.1%
1001
 
< 0.1%
30231
 
0.5%
20459
 
1.0%
10215
 
0.5%
311042
23.9%
222448
48.6%
110896
23.6%

Interactions

2022-06-22T23:53:29.881531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:08.324809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:19.999225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:09.360663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:21.586226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:10.249462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:23.374222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:11.094462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:25.106226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:12.080109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:26.672224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:13.179215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:28.204548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:14.401217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:30.299527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:08.437837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:20.192226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:09.478667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:21.782224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:10.364459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:23.574226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:11.212384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:25.332223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:12.204110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:26.881226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:13.299217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:28.410531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:14.532243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:30.513531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:08.559811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:20.408243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:09.605663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:21.999233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:10.486486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:23.796224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:11.399311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:25.572243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:12.337116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:27.099222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:13.427219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:28.686549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:14.653215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:30.724529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:08.674833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:20.619226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:09.731663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:22.471220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:10.604459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:24.016229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:11.565311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:25.796244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:12.462242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:27.313528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:13.558243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:28.901549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:14.775242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:30.939529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:08.794807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:20.871220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:09.860461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:22.695231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:10.727486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:24.310232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:11.700334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:26.015243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:12.587244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:27.536535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:13.843224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:29.124548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:14.897242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:31.149530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:08.913662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:21.140230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:09.995484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:22.915241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:10.848486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:24.618227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:11.824336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:26.228225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:12.721246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:27.762537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:13.977245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:29.383534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:15.050247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:31.378528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:09.242671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:21.377220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:10.124487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:23.139226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:10.973459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:24.881225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:11.953109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:26.455230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:12.950220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:27.988548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T20:54:14.106241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-22T23:53:29.644531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-06-22T20:54:18.178398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/2022-06-22T23:53:36.543517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-22T20:54:18.463395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-22T23:53:36.775531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-22T20:54:18.627396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-22T23:53:37.302531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-22T20:54:18.766420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-22T23:53:37.534517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-22T20:54:15.230219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-22T23:53:31.690548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-22T20:54:15.452164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-22T23:53:32.066535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-22T20:54:15.623403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-22T23:53:32.360534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-22T20:54:15.703404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-22T23:53:32.499548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IdVentaFechaFecha_EntregaIdCanalIdClienteIdSucursalIdEmpleadoIdProductoPrecioCantidad
012018-03-092018-03-17396913167442817813.122.0
122018-12-282018-12-29288413167442795543.183.0
232016-03-282016-03-312172213167442837430.321.0
342017-10-232017-10-243287613167442834818.842.0
452017-11-222017-11-25267813167442825554.183.0
562018-01-242018-01-252326313167442852152.001.0
672015-03-252015-03-2632983131674429392915.001.0
782017-07-102017-07-182201131674429402162.002.0
892018-04-032018-04-062100613167442905456.003.0
9102019-03-162019-03-171100313167442894515.002.0

Last rows

IdVentaFechaFecha_EntregaIdCanalIdClienteIdSucursalIdEmpleadoIdProductoPrecioCantidad
46170475912020-11-302020-12-07238818366442997485.002.0
46171475922020-11-302020-12-042146319348143016454.001.0
46172475932020-11-302020-12-02298922384242867199.002.0
46173475942020-11-302020-12-0323201233119427632118.381.0
46174475952020-11-302020-12-042293124391942899199.002.0
46175475962020-11-302020-12-09119326303242850279.002.0
46176475972020-11-302020-12-073333263193427991536.042.0
46177475982020-11-302020-12-0712389273667429161356.002.0
46178475992020-11-302020-12-0411067293836429352456.001.0
46179476002020-11-302020-12-06293230386442836823.241.0